Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects

Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the gras...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Makki, Alireza [verfasserIn] Hadi, Alireza Tarvirdizadeh, Bahram Teimouri, Mehdi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Object detection dataset Object dimensions estimation Orientation detection Pose detection Primitive object Sim-to-real transfer

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: BMC Research Notes - London, 2008, 15(2022), 1 vom: 28. Juli
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:1 ; day:28 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s13104-022-06155-4

Katalog-ID:	SPR050888102

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520			\|a Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions.
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allfields	10.1186/s13104-022-06155-4 doi (DE-627)SPR050888102 (SPR)s13104-022-06155-4-e DE-627 ger DE-627 rakwb eng Makki, Alireza verfasserin aut Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213 Hadi, Alireza (orcid)0000-0002-3985-1350 aut Tarvirdizadeh, Bahram aut Teimouri, Mehdi aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 28. Juli (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:28 month:07 https://dx.doi.org/10.1186/s13104-022-06155-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 28 07
spelling	10.1186/s13104-022-06155-4 doi (DE-627)SPR050888102 (SPR)s13104-022-06155-4-e DE-627 ger DE-627 rakwb eng Makki, Alireza verfasserin aut Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213 Hadi, Alireza (orcid)0000-0002-3985-1350 aut Tarvirdizadeh, Bahram aut Teimouri, Mehdi aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 28. Juli (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:28 month:07 https://dx.doi.org/10.1186/s13104-022-06155-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 28 07
allfields_unstemmed	10.1186/s13104-022-06155-4 doi (DE-627)SPR050888102 (SPR)s13104-022-06155-4-e DE-627 ger DE-627 rakwb eng Makki, Alireza verfasserin aut Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213 Hadi, Alireza (orcid)0000-0002-3985-1350 aut Tarvirdizadeh, Bahram aut Teimouri, Mehdi aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 28. Juli (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:28 month:07 https://dx.doi.org/10.1186/s13104-022-06155-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 28 07
allfieldsGer	10.1186/s13104-022-06155-4 doi (DE-627)SPR050888102 (SPR)s13104-022-06155-4-e DE-627 ger DE-627 rakwb eng Makki, Alireza verfasserin aut Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213 Hadi, Alireza (orcid)0000-0002-3985-1350 aut Tarvirdizadeh, Bahram aut Teimouri, Mehdi aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 28. Juli (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:28 month:07 https://dx.doi.org/10.1186/s13104-022-06155-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 28 07
allfieldsSound	10.1186/s13104-022-06155-4 doi (DE-627)SPR050888102 (SPR)s13104-022-06155-4-e DE-627 ger DE-627 rakwb eng Makki, Alireza verfasserin aut Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213 Hadi, Alireza (orcid)0000-0002-3985-1350 aut Tarvirdizadeh, Bahram aut Teimouri, Mehdi aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 28. Juli (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:28 month:07 https://dx.doi.org/10.1186/s13104-022-06155-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 28 07
language	English
source	Enthalten in BMC Research Notes 15(2022), 1 vom: 28. Juli volume:15 year:2022 number:1 day:28 month:07
sourceStr	Enthalten in BMC Research Notes 15(2022), 1 vom: 28. Juli volume:15 year:2022 number:1 day:28 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Object detection dataset Object dimensions estimation Orientation detection Pose detection Primitive object Sim-to-real transfer
isfreeaccess_bool	true
container_title	BMC Research Notes
authorswithroles_txt_mv	Makki, Alireza @@aut@@ Hadi, Alireza @@aut@@ Tarvirdizadeh, Bahram @@aut@@ Teimouri, Mehdi @@aut@@
publishDateDaySort_date	2022-07-28T00:00:00Z
hierarchy_top_id	559431805
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author	Makki, Alireza
spellingShingle	Makki, Alireza misc Object detection dataset misc Object dimensions estimation misc Orientation detection misc Pose detection misc Primitive object misc Sim-to-real transfer Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
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topic_title	Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects Object detection dataset (dpeaa)DE-He213 Object dimensions estimation (dpeaa)DE-He213 Orientation detection (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Primitive object (dpeaa)DE-He213 Sim-to-real transfer (dpeaa)DE-He213
topic	misc Object detection dataset misc Object dimensions estimation misc Orientation detection misc Pose detection misc Primitive object misc Sim-to-real transfer
topic_unstemmed	misc Object detection dataset misc Object dimensions estimation misc Orientation detection misc Pose detection misc Primitive object misc Sim-to-real transfer
topic_browse	misc Object detection dataset misc Object dimensions estimation misc Orientation detection misc Pose detection misc Primitive object misc Sim-to-real transfer
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title	Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
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title_full	Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
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author_browse	Makki, Alireza Hadi, Alireza Tarvirdizadeh, Bahram Teimouri, Mehdi
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title_sort	dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
title_auth	Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
abstract	Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. © The Author(s) 2022
abstractGer	Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. © The Author(s) 2022
abstract_unstemmed	Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions. © The Author(s) 2022
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title_short	Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects
url	https://dx.doi.org/10.1186/s13104-022-06155-4
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author2	Hadi, Alireza Tarvirdizadeh, Bahram Teimouri, Mehdi
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score	7.399811

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Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects

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